Carbon Emission Management Research Articles

Ecosystem Carbon Storage in Hangzhou Bay Area Based on InVEST and PLUS Models

The study of the relationship between the land use and carbon storage of ecosystem services is of great significance to regional carbon emission management. It can provide an important scientific basis for the management of regional ecosystem carbon pools and the formulation of policies for emission reduction and foreign exchange increases. The carbon storage component of the InVEST model and the PLUS model were used to study and predict the temporal and spatial variation characteristics of carbon storage in the ecological system and their relationship with land use type for the periods of 2000-2018 and 2018-2030 in the research area. The results were as follows:the carbon storage in 2000, 2010, and 2018 in the research area was 7.250×108, 7.227×108, and 7.241×108 t, respectively, which suggested that it first decreased and then increased. The change in land use pattern was the main cause of changed carbon storage in the ecological system, and the fast expansion of construction land resulted in the decrease of carbon storage. With its correspondence to land use patterns, the carbon storage in the research area demonstrated significant spatial differentiation and was characterized by low storage in the northeast and high storage in the southwest according to the demarcation line of carbon storage. The resulting prediction was that the carbon storage in 2030 will be 7.344×108 t, with an increase of 1.42% compared with that in 2018, owing mainly to increased forest land. Soil type and population were the two driving factors with the highest contribution to construction land, and soil type and DEM had the highest contribution to forest land.

Spatio-temporal effects of regional resilience construction on carbon emissions: Evidence from 30 Chinese provinces

In response to the threat of rapidly rising carbon emissions, a variety of measures are being implemented to achieve carbon reduction. Resilience construction offers a fresh approach to improving the regional anti-interference ability to cope with various risks, and it is worth considering its impact on carbon emissions. The objective of this study is to investigate the spatio-temporal impacts of resilience construction (RCI) on carbon intensity (CI) in 30 Chinese provinces from 2010 to 2019. The relation pattern between RCI and CI is thoroughly examined after developing a hybrid model by integrating gray correlation analysis (GRA) and coupled coordination degree (CCD). Using the GTWR model, the coefficients reveal the spatio-temporal pattern of the influence of each variable on CI. Furthermore, this study pioneeringly blends GTWR regression results with the K-Means approach to identify areas with homogeneity and heterogeneity of the pattern. Firstly, the findings indicate that there is a significant link between CI and all dimensions -economic resilience (RE), social resilience (RS), and ecological resilience (REn). The relation between REn and CI is the greatest, although it has been declining recently while relations of RS, REn, and CI have all been steadily rising. Secondly, according to the results of CCD, resilience construction and carbon reduction are progressively reaching orderly development but there are still some provinces at low levels of CCD. Thirdly, the study area is divided into four clusters, and the structure of spatial grouping tends to become stable. Moreover, we analyze each cluster's features and suggest appropriate policy measures. The findings aid in the scientific planning of the direction of resilience construction with the goal of collaborative management of carbon emissions.

Research overview of urban carbon emission measurement and future prospect for GHG monitoring network

The majority of anthropogenic carbon dioxide emissions originate from cities, nonetheless, it is common to have inaccuracies and uncertainties in urban carbon emission measurement. Many countries have devoted much effort to improve the accuracy and consistency of urban emission data through improving measurement tools and inversion models. The paper presents achievements of research in the U.S and Europe, as well as discusses their technical impacts and obstacles. The paper provides an overwiew of the technical impacts and research gaps of CO2-USA network and the Carbosense CO2 sensor network in Switzerland, we discuss the spatial and temporal distribution characteristics of greenhouse gas concentrations and explain the principles of carbon emission inversion method. The CO2-USA network applies an interdisciplinary approach to estimate urban carbon emission to simulate the transport process of CO2 molecules in the atmosphere. The Carbosense network employs dense networks to observe the changing process of carbon dioxide emissions via enhancing data quality of low-cost carbon dioxide sensors. The purpose of this paper is to propose a novel framework of GHG observing network which combines carbon emission estimation method and real-time greenhouse gas monitoring network. The framework applies in-situ sites and mobile observations to build high-resolution emissions inventories and gain greenhouse gas concentration mappings in the urban regions. It provides governments with reliable measurement results for planning research directions for carbon emission management in China over the next few years.

Mapping and evaluating sustainable and unsustainable urban areas for ecological management towards achieving low-carbon city: an empirical study of Asir Region, Saudi Arabia

Urbanisation can cause a variety of environmental and health issues, which has prompted experts to evaluate degraded areas and develop management strategies aimed at promoting urban sustainability and reducing carbon emissions. In low-carbon cities, sustainable urban areas have low carbon emission and prioritised carbon reduction by implementing sustainable transportation, green infrastructure, and energy-efficient buildings. On the other hand, unsustainable urban areas tend to lack these priorities and rely heavily on non-renewable energy sources and have high carbon emission. Therefore, this study aims to identify the most sustainable and unsustainable regions in the Abha-Khamis Mushayet Twin City region of Saudi Arabia in respect to urbanisation and carbon emission during the period between 1990 and 2020. To do so, we used Landsat datasets to create land use land cover (LULC) maps and then calculated carbon storage, emission, and absorption using InVest software. Additionally, the study examined micro-climatic conditions by calculating the urban heat island (UHI) effect, which allowed determining sustainable and unsustainable regions by comparing the UHI model and carbon similarity and mismatch model using coupling coordination degree model (CCDM). The study found that during the last three decades, the LULC pattern of the region underwent significant alterations, resulting in a substantial decline in carbon storage from 710,425 Mg C/hm2 in 1990 to approximately 527,012.9 Mg C/hm2 in 2020. Conversely, carbon emissions were observed to be very high in areas with high built-up density, with emission levels exceeding 20 tons per annum. Whilst the areas of excess carbon have decreased significantly, the areas of excess carbon emission have increased over time, resulting in the UHI effect due to high greenhouse gases. By comparing the UHI and carbon similarity and mismatch model, the researchers found that over 280 km2 of the study area is unsustainable and has increased since 1990. In contrast, only about 410 km2 of the study area is currently sustainable. To promote sustainability, the study recommends several strategies such as carbon capture, utilisation, and storage; green infrastructure; and the use of renewable energy to manage carbon emissions.

Research on the Carbon Emission Baselines for Different Types of Public Buildings in a Northern Cold Areas City of China

Global excessive CO2 emissions have caused serious environmental and health problems, such as global warming, melting glaciers, droughts, floods, and extreme temperatures, and have become a common challenge for the world. China has set a dual carbon goal, with the peak carbon emissions before 2030. In China, the building sector accounts for 50.9% of the country’s carbon emissions. In particular, public buildings are characterized by a high carbon emission intensity, accounting for 38.6% of carbon emissions in the building sector, which affects the achievement of the dual carbon goal in China’s building sector. Establishing a reasonable baseline of carbon emissions contributes to quota management and trading of carbon emissions for public buildings in Tianjin, China, and will ultimately contribute to the reduction of carbon emissions. This study investigates the operational energy consumption and carbon emissions of 721 public buildings in Tianjin (including electricity, natural gas, and district heating). The applicability of the Quartile method and the K-means clustering algorithm was compared to determine the carbon emission baseline of different types of public buildings, such as constraint value, guiding value, and advanced value, based on which the dynamic baseline from 2022 to 2030 was determined. The results show that the advanced value, guiding value, and constraint value of the Tianjin public building carbon emission baseline obtained using the Quartile method are more reasonable than those obtained by the K-means clustering algorithm. Furthermore, the carbon emission baseline in 2030 will be reduced by 3.4~9.2% compared to 2022. This study can guide the formulation of carbon emission trading schemes, and support Tianjin’s building sector to achieve the “carbon peak”.

Sustainability reporting, gender diversity, firm value and corporate performance in ASEAN region

This paper aimed to examine the influences of sustainability reporting and gender diversity on corporate outcomes of listed companies in ASEAN region. The listed companies in ASEAN region consisting of Thailand, Malaysia, Indonesia, and the Philippines during 2010 to 2019 were used as population and sample of this study. Seven key performance indexes of the Global Reporting Initiative (GRI) Standards, Carbon Disclosure Project (CDP), Carbon Knights, and Refinitiv were used to measure the extent and level of sustainability reporting, while the proportion of female board committee was used to measure gender diversity, and firm value measured by Tobin’s Q and firm performance measured by return on asset (ROA) were used as the proxies of corporate outcomes. The sequential logit regression models were developed to test the hypotheses used in this study. This study found positive impact of energy used, water management, work safety, and gender diversity on corporate performance, while corporate performance was influenced by carbon emission and waste management negatively. Moreover, gender diversity moderated the negative relationship between energy used, water management, work safety, and corporate performance. On the other hand, there was water management, and work safety influencing firm value positively, while waste management had negative impact on firm value. However, gender diversity did not moderate any relationship between sustainability reporting and firm value. The study findings benefit to top management who may encourage in sustainability reporting especially water management and work safety to enhance the corporate performance and value.

Asset Structure, Asset Utilization Efficiency, and Carbon Emission Performance: Evidence from Panel Data of China’s Low-Carbon Industry

With the development of a low-carbon economy, corporate carbon emission performance has become an important premise for green financing. Compared with high-carbon industries, companies in low-carbon industries have their own carbon advantages and receive less attention. In order to highlight the value of carbon emission performance in low-carbon industries, further investigation on the basis of low-carbon industries is still needed. In terms of fixed assets, which are indicators for an important source of carbon emissions in intensive carbon industries, this study explores the relationship between asset structure, asset utilization efficiency, and carbon emission performance in low-carbon industries. This study selects Chinese listed companies from low-carbon industries that have disclosed their carbon emission performance from 2010 to 2021 as samples. The panel model is used for regression analysis, and then the Arellano-Bover/Blundell-Bond panel dynamic data model is used to solve the problem of endogeneity. The results show that the higher the fixed asset ratio, the worse the carbon emission performance. Asset utilization efficiency weakens the inhibitory effect of the fixed asset ratio on carbon emission performance. This study verifies the significant impact of fixed assets on the carbon emission performance of low-carbon industries as well as the promotion effect of asset utilization efficiency on carbon emission performance. Further investigation verified the promoting effect of corporate growth capabilities on carbon emission performance with two mechanisms, namely the relationship between fixed assets (independent variable)-asset utilization efficiency and (mediator)-corporate growth capabilities (dependent variable) or the relationship between asset utilization efficiency (independent variable)-corporate growth capabilities and (mediator)-fixed assets (dependent variable) from perspectives of enterprise value and expansion. This study expands the influencing factors of carbon emissions in low-carbon industries and is a theoretical supplement to a large number of high-carbon studies. At the same time, it also has certain implications for the carbon emission management practices of enterprises in low-carbon industries. It also reveals the urgency for the government and research institutions to clarify the carbon emission capacity of different fixed assets. Thus, it is convenient for low-carbon industries and high-carbon industries to carry out more refined carbon management and give full play to their carbon advantages.

Research on digital energy carbon management platform and services for energy-using companies under the “3060” target

This study focuses on serving governments, enterprises and individuals related to digital energy carbon management. The National Grid provides digital carbon management services to electricity-using enterprises, solving the problems of high cost, difficulty and long lead time in managing carbon assets of electricity-using enterprises. We have innovated daily real-time carbon emission management services, carbon emission reduction services, carbon decision-making services, value-added carbon asset services and carbon trading services. Xinjiang Autonomous Region is a water-scarce and arid region which water resources are particularly important, so we choose the wastewater treatment industry in Xinjiang as a case study. The daily electricity consumption of Xinjiang Wastewater Treatment Plant A is 4063.75 kWh and the daily electricity consumption carbon emissions are 2.36 tonnes CO2eq, which is estimated to be about 2.78 tonnes CO2eq per day. the daily carbon emissions of the daily electricity consumption Wi of Xinjiang Wastewater Treatment Plant B is 3469.22 kWh and the daily electricity consumption carbon emission is 2.02 tonnes CO2eq, which is estimated to be 2.38 tonnes CO2eq. The carbon account value-added service provides carbon market users with transaction-assisted decision-making services, realizes accurate and efficient services, and helps society achieve the “3060” strategic goal of low-carbon green transformation.

An approach for analyzing urban carbon emissions using machine learning models

Carbon peaking and carbon neutrality goals have posed great challenges to transforming local economies into low-carbon economies. Hence, establishing an effective carbon management system is urgent. However, the development of the urban carbon management system is hampered by the immaturity of the carbon emission accounting system at the city level. To compensate for the insufficiency of the existing urban carbon emission accounting system and to find the city government in constructing a perfect carbon emission management system as soon as possible, this study used the data science method based on the statistical data of 285 cities in China from 2005 to 2017 to explore the influencing factors of urban carbon emissions, that is, using light gradient boosting machine and the accumulated local effects interpretable models to screen potential influencing factors of urban carbon emissions. Then, an index system for urban carbon management was evaluated and proposed, and a case analysis was conducted with urban industrial electricity consumption as a background. This method can be easily integrated with the existing urban management system, which could reduce the cost of building a carbon management system.

Life Cycle Emissions of Passenger Vehicles in China: A Sensitivity Analysis of Multiple Influencing Factors

To reduce greenhouse gas emissions from passenger vehicles, new energy vehicles are actively promoted by China’s government. Various power system types are being developed and their sales keep increasing. However, there is uncertainty about the greenhouse gas emission of different vehicle types. This paper studies the life cycle carbon emissions of passenger vehicles in China. A calculation model is established with consideration of all types of power systems, model classes, and electric driving ranges. In order to calculate and compare the effect of carbon emission reduction on all types of vehicles, a sensitivity analysis is conducted in two ways to study three of the main influencing factors. The results show the carbon emission-reducing effect of different factors on different stages in the life cycle. It is known that different influencing factors have different effects on these stages. Since there is a variation in different vehicle types, the carbon reduction effect caused by these factors is different for these vehicle types. This paper describes a sensitivity analysis of three main influencing factors and puts forward relevant policy recommendations to reduce the carbon emissions of passenger cars during their life cycle based on these results. It is necessary to take the vehicle life cycle as a whole for carbon emission management. The conclusions of this paper can be used for vehicle manufacturers to decide the focus of technology research, and also have important reference significance for enterprises when making life cycle carbon reduction strategies for their products. It is also of certain value for China to formulate a medium- and long-term carbon emission reduction strategy for the passenger car industry.

The optimal carbon tax mechanism for managing carbon emissions

Along Pigouvian lines, the carbon tax not only exceeds the carbon emission damage imposed on society, but ignores the potential cost from deliberate carbon abatement, which in turn challenges the stability and optimality of the Pigouvian solution. For correcting these distortions, this paper amends the standard Pigouvian version by using piecewise tax functions to approximate the social damage curve of carbon emissions. An optimal carbon tax mechanism is designed, where the tax is endogenously determined from social welfare maximization. With the help of a modification instrument, the carbon tax corrects emitters’ non-optimal individual decision and the social optimum is implemented efficiently. How to put the carbon tax into practice is examined under both the deterministic and stochastic modeling settings. In both cases, we demonstrate the structure and effectiveness of the carbon tax in detail. Moreover, a flexible adjustment tax scheme is proposed, which may produce the double-dividend effect that reduces carbon emissions and relieves financial burden of carbon abatement simultaneously. These may improve the application of market-based carbon-reducing tools in public management and pollution regulation.

Corporate governance and carbon emissions performance: International evidence on curvilinear relationships

This study investigates the impact of corporate governance mechanisms (namely board meeting, board independence, board gender diversity, CEO duality, ESG-based compensation and ESG committee) on carbon emissions performance of multinational entities (MNEs). The study analysed international sample of 336 top MNEs operating in 42 non-financial industries from 32 countries over a 15-year period. Result shows that board gender diversity, CEO duality, and ESG committee are negatively associated with carbon emissions rate, whilst board independence and ESG-based compensation have significant positive impact. Whereas board gender diversity and CEO duality have significant negative impact on carbon emissions rate in carbon-intensive industries, the impact of board meeting, board independence and ESG-based compensation is significant and positive. In the non-carbon-intensive industries, board meeting, board gender diversity and CEO duality have significant negative impact on carbon emissions rate, whilst the impact of ESG-based compensation is positive. Further, there is a negative association between the millennium development goals (MDGs)/sustainable development goals (SDGs) era dichotomy and carbon emissions rate, implying that the United Nations agenda for sustainable development significantly affected carbon emissions performance of MNEs, with the SDGs era generally witnessing better carbon emissions management in comparison to the MDGs era in spite of the higher emissions level in the SDGs era. The study contributes to knowledge in several ways. First, it adds to the limited literature on the determinants of carbon emissions reduction within an international context. Second, the study addresses mixed result reported in prior studies. Third, the study adds to knowledge on the governance factors affecting carbon emissions performance in the MDGs and SDGs periods, thus providing evidence on progress MNEs are making towards addressing climate change challenges through carbon emissions management.

Power Battery Recycling Mode and Decision-making Model for New Energy Vehicles Under the Background of Dual Carbon

Recycling and cascade utilization of waste power batteries for new energy vehicles is an effective measure to manage carbon emissions in the power battery industry chain and achieve the dual carbon target. In view of the current confusion of recycling channels and the unclear benefits and costs of the recycling market, the article constructs three recycling modes: self-operated recycling mode, industry alliance recycling mode and third-party enterprise recycling mode. With profit maximization as the decision-making goal, a recycling profit function model is established, and through the comparative analysis of profits of different recycling modes, the relationship and influencing factors between them are obtained, which can provide reference for enterprises to make recycling decisions.

Knowledge Mapping Analysis of Intelligent Ports: Research Facing Global Value Chain Challenges

Integrated technology management in building smart ports or intelligent ports is a crucial concern for global sustainable development, especially when human societies are facing increasing risks from climate change, sea-levels rising, and supply chain disruptions. By mapping the knowledge base of 103 papers on intelligent ports, retrieved in late December 2022 from the Web of Science, this study conducted a roadmapping exercise using knowledge mapping findings, assisted by Bibliometrix, VoSviewer, and customized Python scripts. The three structural (intellectual, social, and conceptual) aspects of knowledge structure reveal the significance of the internet of things (IoT), the fourth industrial revolution (Industry 4.0), digitalization and supply chains, and the need for digital transformation alignment across various stakeholders with Industry 4.0 practices. Furthermore, an even geographical distribution and institutional representation was observed across major continents. The results of the analysis of the conceptual structure demonstrated the existence of several established and emerging clusters of research, namely (1) industry data, IoT, and ICT, (2) industry 4.0, (3) smart airports, (4) automation; and (5) protocol and security. The overall empirical findings revealed the underlying technology and innovation management issues of digital transformation alignment across stakeholders in IoT, Industry 4.0, 5G, Big Data, and AI integrated solutions. In relation to roadmapping, this study proposed a socio-technical transition framework for prototyping ecosystem innovations surrounding smart sustainable ports, focusing on contributing to valuable carbon or greenhouse gas emission data governance, management, and services in global value chains.

Influencing factors and scenario forecasting of carbon emissions in Liaoning Province, China

Liaoning is a province with large energy consumption and carbon emissions. Management of carbon emissions in Liaoning Province is crucial to realizing China's carbon peaking and carbon neutrality goals. To clarify the driving factors and trends of carbon emissions in Liaoning Province, we analyzed the impacts of six factors on carbon emissions in Liaoning Province through STIRPAT model based on carbon emission data from 1999 to 2019. The impact factors included population, urbanization rate, per-capita GDP, secondary industry ratio, energy consumption per unit GDP, and coal consumption ratio. Nine forecasting scenarios with three economic and population growth models and three emission reduction models were set up, and their carbon emission trends under the above nine forecasting scenarios were predicted. The results showed that the main driving factor of carbon emissions in Liaoning Province was per-capita GDP, and that the main inhibitor was energy consumption per unit GDP. The carbon peak year in Liaoning Province would fluctuate between 2020 and 2055 under the nine forecasting scenarios, with peak values ranging from 544 to 1088 million tons CO2. The medium economic development growth and high carbon emission reduction scenario would be the optimal carbon emission scenario in Liaoning Province. Under this forecasting scenario, Liaoning Province could achieve carbon peak (611 million tons CO2) by 2030 without affec-ting economic development through optimizing energy structure and controlling the intensity of energy consumption. Our results would be helpful for seeking the best path for carbon emission reduction in Liaoning Province and providing a reference for its realization of carbon peaking and carbon neutrality goals.

Research on Comprehensive Evaluation of Near-zero Carbon Emission Parks Based on Variable Weight Theory

Near-zero carbon emissions are the further deepening and implementation of low-carbon emissions and a dynamic process constantly moving towards zero carbon emissions. With the carbon formation life cycle as the core, the low-carbon park is divided into smart parks and ecological parks. They are starting from the four elements of planning, near-zero carbon technology, carbon emission management, and environmental health, and forming an index system architecture according to the park’s carbon emission traceability tracking system. When the park is in different stages of near-zero carbon development, the internal differences of evaluation indicators are also different. The constant weight evaluation method cannot reflect the impact of each indicator on the development of near-zero carbon, which will affect the accuracy of the evaluation results. By introducing variable weight theory, a penalty variable weight model is constructed, and the model is applied to the case park. Comparing the results of the constant-weight evaluation and variable-weight evaluation, it can be seen that the evaluation results of the variable-weight evaluation model are easier to distinguish the development level. The verification results show that the variable weight evaluation method is more suitable for the dynamic characteristics of near-zero carbon development.

Analysis of spatial and temporal characteristics of carbon emission efficiency of pig farming and the influencing factors in China.

Pig farming has been a crucial contribution to China's food security although intestinal fermentation and its excrement during pig breeding are major sources of greenhouse gas emissions. In this paper, we measured the carbon emission efficiency of pig farming in 30 provinces (autonomous regions and municipalities) from 2010 to 2020 by using the non-expected output Slack-Based Measure (SBM) model and analyzed the spatial characteristics of the carbon emission efficiency of pig farming in China. We also examined and analyzed the factors influencing the carbon emission efficiency of pig farming by using the limited dependent variable model (Tobit). The results show that: the carbon emission efficiency of pig farming in China shows an M-shaped upward trend over time by comparing the carbon emission efficiency longitudinally during the study period and the carbon emission efficiency of pig farming shows a decreasing trend in the east, central and west regions of China by comparing the carbon emission efficiency of different regions horizontally. It's also shown that regions with low- and extremely-low-efficiency transfer from the east to the central and west regions and the central and regions with high-efficiency transfer to the east. The regression analysis of the factors influencing the carbon emission efficiency of pig breeding shows that the comparative advantage of the pig industry and transportation accessibility is positively correlated with the carbon emission efficiency of pig breeding, whereas the proportion of food resources and market scale is negatively correlated with the carbon emission efficiency of pig breeding. At the same time, the production layout index has no significant influence on the carbon emission efficiency of pig breeding. The research results provide a theoretical basis for regional differentiation of carbon emission management from pig farming, optimizing the layout of the pig industry and reducing environmental pollution.

Water-climate change extended nexus contribution to social welfare and environment-related sustainable development goals in China.

Climate change exacerbates uncertainties in water resource management, water supply, and treatment that are energy intensive and then exert great pressure on climate change mitigation; hence, interrelated and contradictory characteristics within the water-climate change (WC) nexus system are needed to be studied. The nexus thinking and coordination of WC would impact many realistic practices and assist in sustainable socioeconomic development since traditional single-target policies have sometimes been out of function. Hence, the ability to direct water production and use as well as climate change mitigation has become a hotspot recently. Furthermore, we find that there has been no complete research on reviewing the impacts of the WC nexus in different areas on the Sustainable Development Goals (SDGs). Hence, this paper builds a core nexus of WC and then analyzes those effects on social and environmental aspects in many areas, including sewage treatment, energy transition, waste treatment, land management, and ocean management. This paper discusses how WC interlinkages are utilized to realize SDGs in those areas. Moreover, uncertainties derived from exogenous hydrology, climate change, and anthropogenic endogenous systems for realistic problems appeal to gradually increasing concern. Finally, implications offer valuable guidelines for integrated management of water and carbon emissions, as well as sustainable socioeconomic development in the future.

Design of Substation Carbon Emission Prediction Model Based on Cloud Model

In order to achieve the goal of energy conservation and emission reduction, all walks of life are taking active actions. Power system, as the main dispatching application of energy, is in full swing in the monitoring and management of carbon emissions and other related studies. The purpose of this paper is to study the design of substation carbon emission prediction model based on the cloud model. First, the basic concepts and numerical characteristics of the cloud model are introduced. Secondly, it introduces the principle of carbon emission prediction method based on cloud model and the common carbon emission prediction method. Combining the real-time substation carbon emission data simulation with the cloud model prediction, the prediction results were compared with the BP neural network, and two average error values were used to measure the prediction results. It was found that the cloud model had a higher accuracy in predicting carbon emission, which verified its feasibility and superiority.

Research on Carbon Emission Management of Electric Power Enterprises Based on Blockchain Technology

As a high energy-consuming industry, it is especially important for electric power companies to manage carbon emissions well. This study analyzes the main problems of electric power companies in carbon asset management, and investigates the methods of intelligent, digital and transparent management of carbon emission situation using intelligent Internet of Things and blockchain technology. Carbon emission management based on blockchain technology can improve the accuracy and openness of carbon asset data and promote electric power companies to move forward to the low-carbon ecological era.